This course, "Autoencoders | Let's understand how to code autoencoders in Python," is designed to provide an in-depth understanding of autoencoders and to teach you how to code them using Python. Autoencoders are a type of neural network that can be used for various purposes, such as data compression, denoising, and feature extraction. Throughout the course, you will learn the theoretical concepts behind autoencoders, including the architecture and key components. You will also gain practical experience by implementing autoencoders in Python. The course will start with an introduction to the basics of neural networks and deep learning, ensuring you have the fundamental knowledge required to dive into autoencoders. You will then delve into the theory of autoencoders, exploring different types, such as basic autoencoders, denoising autoencoders, and variational autoencoders. Next, you will learn how to preprocess data for training an autoencoder and understand the importance of feature scaling and normalization. You will then proceed to build and train various types of autoencoders using Python libraries such as TensorFlow and Keras. The course will cover topics like setting up the architecture, training the model, and evaluating its performance. In addition to coding autoencoders, you will also discover practical use cases and applications, such as image compression, anomaly detection, and dimensionality reduction. You will explore various real-world examples and understand how to adapt autoencoders to different scenarios. By the end of this course, you will have a solid understanding of autoencoders and be able to implement them efficiently in Python. Whether you are a beginner or an experienced Python programmer, this course will equip you with the skills and knowledge to leverage the power of autoencoders in your projects or research.